Call for papers: "Machine Learning and the Internet of Things"

For a special issue of the International Journal of Internet of Things and Cyber-Assurance.

Machine learning through computer systems, which propagates from network to network, is at the heart of computer intelligence. Machine learning is the key to simplifying the definition of a problem-solving platform. Basically, it is a mechanism for pattern search and building intelligence into a computer (e.g. machine) to be able to learn, implying that it will be able to do better in the future from its own experience.

This special issue aims to present machine learning research pertaining to the Internet of Things (IoT). Machines learning from IoT devices, networks and data, in particular to detect and unveil possible hidden structures and regularity patterns associated with their generation mechanism, is important. This issue will promote analysis and understanding of the nature of the machine learning data, which can be used to make predictions for future decisions and actions for computer processing. Its objective is to develop and publish efficient algorithms for designing models and analysis for machine learning prediction and to present research on how to analyse data for such applications in a way that meets demands for algorithms to be computationally efficient and at the same time robust in their performance.

The issue will carry revised and substantially extended versions of selected papers presented at 2nd International Conference on Research in Intelligent and Computing in Engineering (RICE-2017), but we also strongly encourage researchers unable to participate in the conference to submit articles for this call.

Suitable topics include, but are not limited to, the following:

• Internet of Things
• Smart cities
• Big data
• Machine learning
• Medicine, health, bioinformatics and systems biology
• Industrial and engineering applications
• Security applications
• Game playing and problem solving
• Intelligent virtual environments
• Economics, business and forecasting applications, etc.
• Distributed and parallel learning algorithms and applications
• Feature extraction and classification
• Neural networks
• Theories and models for plausible reasoning including:
• computational learning theory
• cognitive modelling
• Hybrid learning algorithms

Important Dates
Submission of manuscripts: 15 May, 2017
Notification to authors: 15 July, 2017
Final versions due: 15 September, 2017